Digital Imaging The availability of digital cameras -from dedicated photographic cameras to cell phones -has quickly decreased the use of film-based imaging. The development of medical imaging too has undergone a rapid transition in the same direction, one of enhancement. Some imaging modalities, e.g. computed tomography, scanning laser ophthalmoscopy (SLO) and optical coherence tomography rely on digital imaging, in contrast to, fundus photography and fluorescein angiography which appeared

more »

... e early, do not. The first photographic images of the ocular fundus were obtained by the end of the 19th and the beginning of the 20th centuries, and the concept of a fundus camera dates back to that time [1] . As stated in a recent review [2] : 'The primary role of ophthalmic imaging however, goes well beyond documentation in its ability to aid in the diagnosis of a broad range of eye conditions'. Additional continuous efforts have been made to achieve the best possible fundus images [3] . This review focuses on digital imaging of the human eye fundus and its impact on clinical use. It does not attempt to provide an exhaustive description of all digital imaging modalities with application to the human ocular fundus; instead, it concentrates primarily on fundus photography. It establishes a link between traditional (analog/nondigital) and digital imaging, and addresses intrinsic differences, advantages and disadvantages of each. Abstract Ocular fundus imaging plays a key role in monitoring the health status of the human eye. Currently, a large number of imaging modalities allow the assessment and/or quantification of ocular changes from a healthy status. This review focuses on the main digital fundus imaging modality, color fundus photography, with a brief overview of complementary techniques, such as fluorescein angiography. While focusing on two-dimensional color fundus photography, the authors address the evolution from nondigital to digital imaging and its impact on diagnosis. They also compare several studies performed along the transitional path of this technology. Retinal image processing and analysis, automated disease detection and identification of the stage of diabetic retinopathy (DR) are addressed as well. The authors emphasize the problems of image segmentation, focusing on the major landmark structures of the ocular fundus: the vascular network, optic disk and the fovea. Several proposed approaches for the automatic detection of signs of disease onset and progression, such as microaneurysms, are surveyed. A thorough comparison is conducted among different studies with regard to the number of eyes/subjects, imaging modality, fundus camera used, field of view and image resolution to identify the large variation in characteristics from one study to another. Similarly, the main features of the proposed classifications and algorithms for the automatic detection of DR are compared, thereby addressing computer-aided diagnosis and computer-aided detection for use in screening programs.